Main Variables poulation
Total biomass
Data used en both (spatial and No spatial models)
Respecto a los valores y parametros biologicos modelados, los siguientes graficos identifican los estimadores puntuales del recurso
Integrated approach to modeling to krill population dynamics in Wester Antarctic Peninsula: spatial and ecosystem considerations
Working Paper to be submitted in a CCAMLR EMM-WG 2024
Mardones, M; Cárdenas, C., Krüger, L., Santa Cruz, F.
06 June, 2024
## [1] 16dir01 <- here("s01") # agreggate data (no spatial diferences)
dir1 <- here("s1") # Data strata flishery
dir2 <- here("s2") # Same 9 with areas (SubStrata) as fleet. Dif size comoposition and dif CPUE and dif survey length and biomass data by strata
dir3 <- here("s3") # whitout S-R
dir4 <- here("s4") #
dir5 <- here("s5") #
dir6 <- here("s6") #
dir7 <- here("s7") # 2 set parametres EMM-2024/23 (Mardones)
dirtest <- here("test")
Figs <- here("Figs") # SOVERVIEW
In a simple way, the core of Stock Synthesis is its population dynamics model, which represents the dynamics of krill populations over time. This model incorporates key biological, environmental and predator data sources. The model is typically formulated using mathematical equations that describe how these parameters interact to determine the abundance and distribution of krill in the study area.
Statistical model (SS3)
Stock Synthesis v.3.30.21 is a widely used software tool for assessing fish and invertebrate populations, including krill (Euphausia superba) in the Antarctic Peninsula region. The methodology employed by Stock Synthesis involves a comprehensive and integrated approach, utilizing various data sources and modeling techniques to estimate the main population variables of krill in WAP.
The stock assesment model was configured using Stock Synthesis (SS3
hereafter)SS3
(Richard Donald
Methot et al., 2020; Richard D. Methot
& Wetzel, 2013) with the most updated version
(V.3.30.21). SS3 is a structured age and size stock evaluation model, in
the class of models called “Integrated stock evaluation analysis
model”. SS3 has a stock population sub-model that simulates growth,
maturity, fecundity, recruitment, movement, and mortality processes, and
observation sub-models and expected values for different types of data.
The model is coded in C++ with estimation parameters
enabled by automatic differentiation (ADMB) (Fournier et al., 2012; Richard D. Methot & Wetzel, 2013).
The analysis of results and outputs uses R tools and the graphical
interface of the r4ss and ss3diags library (Taylor, 2019; Winker et al., 2024).
By integrating data from multiple sources and considering spatial heterogeneity, the assessment methodology using Stock Synthesis v.3.30.21 provides a robust framework for evaluating the status of krill populations in the Antarctic Peninsula region. This information is essential for supporting management decisions aimed at ensuring the sustainable use of krill resources in this ecologically sensitive area.
Parametres
read files
| LO | HI | INIT | PHASE | |
|---|---|---|---|---|
| Mortalidad natural | ||||
| Nat M | 0.20 | 0.50 | 0.400000 | 3 |
| Crecimiento | ||||
| Lmin | 0.00 | 3.00 | 1.000000 | 2 |
| Lmax | 1.00 | 10.00 | 7.000000 | 4 |
| VonBert K | 0.05 | 0.80 | 0.450000 | 4 |
| CV young | 0.05 | 0.25 | 0.050000 | -3 |
| CV old | 0.05 | 0.25 | 0.050000 | -3 |
| Relación longitud-peso | ||||
| Wt a | 0.00 | 3.00 | 0.000005 | -3 |
| Wt b | 1.00 | 4.00 | 3.346940 | -3 |
| Ojiva de madurez | ||||
| L50% | 0.20 | 5.00 | 2.400000 | 4 |
| Mat slope | -3.00 | 3.00 | -0.250000 | 4 |
| Relación stock-recluta | ||||
| SR_LN(R0) | 3.00 | 30.00 | 10.000000 | 1 |
| SR_BH_steep | 0.20 | 1.00 | 0.750000 | -4 |
| SR_sigmaR | 0.00 | 2.00 | 0.600000 | -4 |
| SR_regime | -5.00 | 5.00 | 0.000000 | -4 |
| SR_autocorr | 0.00 | 0.00 | 0.000000 | -99 |
| Capturabilidad | ||||
| LnQ_base_FISHERYBS(1) | -25.00 | 25.00 | -3.722310 | 1 |
| Q_extraSD_FISHERYBS(1) | 0.00 | 1.00 | 0.000000 | 3 |
| Selectividad | ||||
| SizeSel_P_1_FISHERYBS(1) | 0.01 | 8.00 | 2.500000 | 3 |
| SizeSel_P_2_FISHERYBS(1) | 1.50 | 8.00 | 2.000000 | 2 |
| SizeSel_P_1_FISHERYEI(2) | 0.01 | 8.00 | 2.500000 | 3 |
| SizeSel_P_2_FISHERYEI(2) | 1.50 | 8.00 | 2.000000 | 2 |
| SizeSel_P_1_FISHERYGS(3) | 0.01 | 8.00 | 2.500000 | 3 |
| SizeSel_P_2_FISHERYGS(3) | 1.50 | 8.00 | 2.000000 | 2 |
| SizeSel_P_1_FISHERYJOIN(4) | 0.01 | 8.00 | 2.500000 | 3 |
| SizeSel_P_2_FISHERYJOIN(4) | 1.50 | 8.00 | 2.000000 | 2 |
| SizeSel_P_1_FISHERYSSIW(5) | 0.01 | 8.00 | 2.500000 | 3 |
| SizeSel_P_2_FISHERYSSIW(5) | 1.50 | 8.00 | 2.000000 | 2 |
| SizeSel_P_1_SURVEYBS(6) | 1.00 | 7.00 | 2.000000 | 2 |
| SizeSel_P_2_SURVEYBS(6) | 1.00 | 7.00 | 1.000000 | 3 |
| SizeSel_P_1_SURVEYEI(7) | 1.00 | 7.00 | 2.000000 | 2 |
| SizeSel_P_2_SURVEYEI(7) | 1.00 | 7.00 | 1.000000 | 3 |
| SizeSel_P_1_SURVEYGS(8) | 1.00 | 7.00 | 2.000000 | 2 |
| SizeSel_P_2_SURVEYGS(8) | 1.00 | 7.00 | 1.000000 | 3 |
| SizeSel_P_1_SURVEYJOIN(9) | 1.00 | 7.00 | 2.000000 | 2 |
| SizeSel_P_2_SURVEYJOIN(9) | 1.00 | 7.00 | 1.000000 | 3 |
| SizeSel_P_1_SURVEYSSIW(10) | 1.00 | 7.00 | 2.000000 | 2 |
| SizeSel_P_2_SURVEYSSIW(10) | 1.00 | 7.00 | 1.000000 | 3 |
| SizeSel_P_1_PREDATOR(11) | 0.00 | 7.00 | 0.500000 | -2 |
| SizeSel_P_2_PREDATOR(11) | 1.00 | 7.00 | 3.500000 | -3 |
Scenarios
In Table 1 we have ten scenarios to test different option in modeling about main consideration in assessment of krill population.
| Scenario | Description |
|---|---|
| s01 | Fishery and Survey (AMLR) data, Predator, Environmental aggregate data in 48.1 |
| s1 | Fishery and Survey (AMLR) data Length, Index, Catch by strata. Predator and Env data |
| s2 | “s1” without S-R relation |
| s3 | “s1” BH S-R relation weak (0.9 steepness) |
| s4 | “s1” BH S-R relation strong (0.6 steepness) |
| s5 | “s1” BH S-R relation mid-strong estimated |
| s6 | “s1” Ricker S-R relation estimated |
| s7 | “s1” w/ set of parameters estimated in (EMM-204/32?) |
Run Models
Read outputs
RESULTS
Diagnosis Base Model
Step to do a good practice in model diagnosis is;
1. Convergence. Final convergence criteria is 1.0e-042. Residual (visual and metrics)3. Retrospective analysis (Mhon Parameter)- Likelihood profile
all this framework try to follow recommendations of Carvalho et al. (2021)
Residual consistency
##
## Running Runs Test Diagnosics for Mean length
## Plotting Residual Runs Tests
##
## Runs Test stats by Mean length:## Index runs.p test sigma3.lo sigma3.hi type
## 1 FISHERYBS 0.500 Passed -0.1816665 0.1816665 len
## 2 FISHERYEI 0.145 Passed -0.2319052 0.2319052 len
## 3 FISHERYGS 0.338 Passed -0.1813395 0.1813395 len
## 4 FISHERYJOIN NA Excluded NA NA len
## 5 FISHERYSSIW 0.406 Passed -0.1476043 0.1476043 len
## 6 SURVEYBS 0.189 Passed -0.2452391 0.2452391 len
## 7 SURVEYEI 0.148 Passed -0.2482065 0.2482065 len
## 8 SURVEYGS 0.334 Passed -0.3723597 0.3723597 len
## 9 SURVEYJOIN 0.500 Passed -0.5749614 0.5749614 len
## 10 PREDATOR 0.599 Passed -0.3154527 0.3154527 len##
## Running Runs Test Diagnosics for Index
## Plotting Residual Runs Tests
##
## Runs Test stats by Index:## Index runs.p test sigma3.lo sigma3.hi type
## 1 FISHERYBS 0.010 Failed -0.8521502 0.8521502 cpue
## 2 FISHERYEI 0.179 Passed -1.1048793 1.1048793 cpue
## 3 FISHERYGS 0.018 Failed -1.9450662 1.9450662 cpue
## 4 FISHERYJOIN 0.819 Passed -1.9136749 1.9136749 cpue
## 5 FISHERYSSIW 0.025 Failed -0.5893076 0.5893076 cpue
## 6 SURVEYBS 0.278 Passed -2.8964975 2.8964975 cpue
## 7 SURVEYEI 0.708 Passed -2.5384240 2.5384240 cpue
## 8 SURVEYGS 0.753 Passed -3.2473271 3.2473271 cpue
## 9 SURVEYJOIN 0.268 Passed -2.6879583 2.6879583 cpue
## 10 SURVEYSSIW NA Excluded NA NA cpue
## 11 PREDATOR 0.002 Failed -0.7168626 0.7168626 cpue## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERY 44.7 21
## 2 SURVEY1 82.6 24
## 3 PREDATOR 25.8 41
## 4 Combined 52.1 86## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 54.3 18
## 2 FISHERYEI 40.7 20
## 3 FISHERYGS 72.8 14
## 4 FISHERYJOIN 65.6 6
## 5 FISHERYSSIW 33.8 21
## 6 SURVEYBS 102.9 21
## 7 SURVEYEI 80.5 18
## 8 SURVEYGS 111.5 20
## 9 SURVEYJOIN 108.4 8
## 10 SURVEYSSIW 52.6 2
## 11 PREDATOR 39.4 41
## 12 Combined 71.3 189## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 54.2 18
## 2 FISHERYEI 40.8 20
## 3 FISHERYGS 72.5 14
## 4 FISHERYJOIN 66.2 6
## 5 FISHERYSSIW 33.8 21
## 6 SURVEYBS 102.5 21
## 7 SURVEYEI 80.3 18
## 8 SURVEYGS 111.5 20
## 9 SURVEYJOIN 108.1 8
## 10 SURVEYSSIW 53.3 2
## 11 PREDATOR 39.4 41
## 12 Combined 71.2 189## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 54.2 18
## 2 FISHERYEI 40.8 20
## 3 FISHERYGS 72.5 14
## 4 FISHERYJOIN 66.2 6
## 5 FISHERYSSIW 33.8 21
## 6 SURVEYBS 102.5 21
## 7 SURVEYEI 80.3 18
## 8 SURVEYGS 111.5 20
## 9 SURVEYJOIN 108.1 8
## 10 SURVEYSSIW 53.3 2
## 11 PREDATOR 39.4 41
## 12 Combined 71.2 189## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 57.3 18
## 2 FISHERYEI 39.9 20
## 3 FISHERYGS 79.3 14
## 4 FISHERYJOIN 50.7 6
## 5 FISHERYSSIW 35.4 21
## 6 SURVEYBS 111.9 21
## 7 SURVEYEI 87.7 18
## 8 SURVEYGS 115.8 20
## 9 SURVEYJOIN 114.3 8
## 10 SURVEYSSIW 37.5 2
## 11 PREDATOR 39.4 41
## 12 Combined 74.9 189## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 71.4 18
## 2 FISHERYEI 46.5 20
## 3 FISHERYGS 98.4 14
## 4 FISHERYJOIN 39.6 6
## 5 FISHERYSSIW 48.0 21
## 6 SURVEYBS 118.7 20
## 7 SURVEYEI 101.1 18
## 8 SURVEYGS 110.3 19
## 9 SURVEYJOIN 136.3 8
## 10 SURVEYSSIW 14.5 2
## 11 PREDATOR 29.9 41
## 12 Combined 80.5 187## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 72.5 18
## 2 FISHERYEI 47.9 20
## 3 FISHERYGS 100.2 14
## 4 FISHERYJOIN 37.4 6
## 5 FISHERYSSIW 49.1 21
## 6 SURVEYBS 120.8 20
## 7 SURVEYEI 105.1 18
## 8 SURVEYGS 112.5 19
## 9 SURVEYJOIN 138.8 8
## 10 SURVEYSSIW 19.6 2
## 11 PREDATOR 39.4 41
## 12 Combined 83.1 187## Plotting JABBA residual plot
##
## RMSE stats by Index:## indices RMSE.perc nobs
## 1 FISHERYBS 56.2 18
## 2 FISHERYEI 40.0 20
## 3 FISHERYGS 77.3 14
## 4 FISHERYJOIN 53.6 6
## 5 FISHERYSSIW 34.7 21
## 6 SURVEYBS 109.2 21
## 7 SURVEYEI 86.5 18
## 8 SURVEYGS 115.0 20
## 9 SURVEYJOIN 112.2 8
## 10 SURVEYSSIW 42.7 2
## 11 PREDATOR 39.4 41
## 12 Combined 73.9 189Relationship Stock-Recruit
Retrospective analysis
Los análisis retrospectivo, dan cuenta de diferencias de estimación (sub - sobreestimación) en los patrones entre modelos evaluados.
Hindcast Cross-Validation and prediction skill
Implementing the Hindcast Cross-Validation (HCxval) diagnostic in
Stock Synthesis requires the same model outputs generated by
r4ss:SS_doRetro(). As a robust measure of prediction skill,
we implemented the mean absolute scaled error (MASE). In brief, the MASE
score scales the mean absolute. Regarding (A MASE score > 1 indicates
that the average model forecasts are worse than a random walk.
Conversely, a MASE score of 0.5 indicates that the model forecasts twice
as accurately as a naïve baseline prediction; thus, the model has
prediction skill.
Kobe (status)
##
## starter.sso with Bratio: SSB/SSB0 and F: _abs_F
## 
another
Likelihood
Convergence criteria
0.0001 final convergence criteria (e.g. 1.0e-04)
Outputs Model Base
| Yr | Era | Seas | Bio_all | Bio_smry | SpawnBio | Recruit_0 | |
|---|---|---|---|---|---|---|---|
| 1679 | 1976 | VIRG | 1 | 3244530 | 3235450 | 1144730 | 1319640000 |
| 1680 | 1977 | INIT | 1 | 3244530 | 3235450 | 1144730 | 1319640000 |
| 1681 | 1978 | TIME | 1 | 3249980 | 3235450 | 1144730 | 2112190000 |
| 1682 | 1979 | TIME | 1 | 3307530 | 3287650 | 1144730 | 2890610000 |
| 1683 | 1980 | TIME | 1 | 3450930 | 3441500 | 1144340 | 1371580000 |
| 1684 | 1981 | TIME | 1 | 3428230 | 3424240 | 1083240 | 580747000 |
| 1685 | 1982 | TIME | 1 | 3449600 | 3447050 | 1075260 | 371065000 |
| 1686 | 1983 | TIME | 1 | 3179940 | 3176410 | 1019690 | 512862000 |
| 1687 | 1984 | TIME | 1 | 2974100 | 2963260 | 1027460 | 1575930000 |
| 1688 | 1985 | TIME | 1 | 2862610 | 2858110 | 1096720 | 654349000 |
| 1689 | 1986 | TIME | 1 | 2678740 | 2674570 | 1121100 | 606217000 |
| 1690 | 1987 | TIME | 1 | 2338860 | 2331870 | 957885 | 1017320000 |
| 1691 | 1988 | TIME | 1 | 2224460 | 2214040 | 851661 | 1514860000 |
| 1692 | 1989 | TIME | 1 | 2115770 | 2098060 | 739424 | 2574280000 |
| 1693 | 1990 | TIME | 1 | 2160480 | 2149170 | 676793 | 1645000000 |
| 1694 | 1991 | TIME | 1 | 2175200 | 2170750 | 638855 | 647486000 |
| 1695 | 1992 | TIME | 1 | 2180870 | 2178150 | 574259 | 395134000 |
| 1696 | 1993 | TIME | 1 | 2168580 | 2161130 | 560105 | 1082420000 |
| 1697 | 1994 | TIME | 1 | 2244840 | 2233240 | 632549 | 1686260000 |
| 1698 | 1995 | TIME | 1 | 2339890 | 2329540 | 738052 | 1505020000 |
| 1699 | 1996 | TIME | 1 | 2448890 | 2442610 | 860906 | 913517000 |
| 1700 | 1997 | TIME | 1 | 2521570 | 2517950 | 883197 | 526178000 |
| 1701 | 1998 | TIME | 1 | 2508190 | 2504250 | 832332 | 573318000 |
| 1702 | 1999 | TIME | 1 | 2462280 | 2447050 | 799843 | 2213040000 |
| 1703 | 2000 | TIME | 1 | 2520000 | 2504520 | 846772 | 2250720000 |
| 1704 | 2001 | TIME | 1 | 2668140 | 2653410 | 916344 | 2141880000 |
| 1705 | 2002 | TIME | 1 | 2855370 | 2850270 | 935110 | 741167000 |
| 1706 | 2003 | TIME | 1 | 2985590 | 2978960 | 902888 | 965164000 |
| 1707 | 2004 | TIME | 1 | 3057050 | 3049840 | 879122 | 1048360000 |
| 1708 | 2005 | TIME | 1 | 3101830 | 3085190 | 914406 | 2418740000 |
| 1709 | 2006 | TIME | 1 | 3259220 | 3244510 | 1084860 | 2138280000 |
| 1710 | 2007 | TIME | 1 | 3330550 | 3322930 | 1158320 | 1108000000 |
| 1711 | 2008 | TIME | 1 | 3435280 | 3423810 | 1197020 | 1666820000 |
| 1712 | 2009 | TIME | 1 | 3479050 | 3460490 | 1124160 | 2698050000 |
| 1713 | 2010 | TIME | 1 | 3589180 | 3569310 | 1104040 | 2888260000 |
| 1714 | 2011 | TIME | 1 | 3672680 | 3654360 | 1083760 | 2663620000 |
| 1715 | 2012 | TIME | 1 | 3988290 | 3982610 | 1205830 | 825836000 |
| 1716 | 2013 | TIME | 1 | 4136720 | 4127180 | 1254410 | 1386190000 |
| 1717 | 2014 | TIME | 1 | 4060520 | 4046150 | 1210280 | 2089470000 |
| 1718 | 2015 | TIME | 1 | 4025220 | 4013810 | 1254770 | 1659130000 |
| 1719 | 2016 | TIME | 1 | 3967690 | 3960880 | 1346260 | 989842000 |
| 1720 | 2017 | TIME | 1 | 3863280 | 3855110 | 1396420 | 1188290000 |
| 1721 | 2018 | TIME | 1 | 3689970 | 3682420 | 1373480 | 1098610000 |
| 1722 | 2019 | TIME | 1 | 3507920 | 3502770 | 1265680 | 748826000 |
| 1723 | 2020 | TIME | 1 | 3346120 | 3343480 | 1234730 | 384541000 |
| 1724 | 2021 | FORE | 1 | 3177260 | 3168120 | 1247410 | 1328760000 |
| 1725 | 2022 | FORE | 1 | 2968650 | 2959540 | 1193720 | 1324170000 |
| 1726 | 2023 | FORE | 1 | 2782530 | 2773490 | 1093730 | 1314540000 |
| 1727 | 2024 | FORE | 1 | 2674910 | 2665920 | 1020370 | 1306380000 |
| 1728 | 2025 | FORE | 1 | 2613310 | 2604390 | 944772 | 1296770000 |
| NA | NA | NA | NA | NA | NA | NA | NA |
| NA.1 | NA | NA | NA | NA | NA | NA | NA |
| NA.2 | NA | NA | NA | NA | NA | NA | NA |
Comparision outputs betwwen scenarios
comparision between select models No spatialand
Spatial implicit and
Spatial W/ new set parametres
comparision between select models Old Paramters and
New Parameters WG SAM 2024/23
Comparsion in sd long term time series
Autocorrelation in Recruit and Biomas
Recruit deviation
REFERENCES
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Methot, R. D. (2021). A cookbook for using model
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Processed Report Series NMFS-NWFSC-PR-2020-05]. U.S. Department of
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Installing R and getting R4SS. Fisheries Science,
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ss3diags: What the package does (one line, title case). https://github.com/jabbamodel/ss3diags